Extending Grammatical Evolution with Attribute Grammars: An Application to Knapsack Problems

Research extending the capabilities of the well-known evolutionary-algorithm (EA) of Grammatical Evolution (GE) is presented. GE essentially describes a software component for (potentially) any search algorithm (more prominently an EA) whereby it serves to facilitate the generation of viable solutions to the problem at hand. In this way, GE can be thought of as a generallyapplicable, robust and pluggable component to any search-algorithm. Facilitating this plug-ability is the ability to hand-describe the structure of solutions to a particular problem; this, under the guise of the concise and effective notation of a grammar definition. This grammar may be thought of, as the rules for the generation of solutions to a problem. Recent research has shown, that for static-problems (problems who’s optimum-solution resides within a finitely-describable set, for the set of allpossible solutions), the ability to focus the search (for the optimum) on the more promising regions of this set, has provided the best-performing approaches to-date. As such, it is suggested that search be biased toward more promising areas of the set of all possible solutions. In it’s use of a grammar, GE provides such a bias (as a language-bias), yet remains unable, to effectively bias the search for problems of constrainedoptimisation. As such, and as detailed in this thesis the mechanism of an attribute grammar is proposed to maintain GE as a pluggable component